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Matrix Visualization and Information Mining

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Book cover COMPSTAT 2004 — Proceedings in Computational Statistics

Abstract

Many statistical techniques, particularly multivariate methodologies, focus on extracting information from data and proximity matrices. Rather than rely solely on numerical characteristics, matrix visualization allows one to graphically reveal structure in a matrix.This article reviews the history of matrix visualization, then gives a more detailed description of its general framework, along with some extensions. Possible research directions in matrix visualization and information mining are sketched. Color versions of figures presented in this article, together with software packages, can be obtained from http://gap.stat.sinica.edu.tw/.

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Author information

Authors and Affiliations

  1. Academia Sinica, Institute of Statistical Science, Taipei, Taiwan

    Chun-Houh Chen, Wen-Jung Jang, Chiun-How Kao, ShengLi Tzeng & Han-Ming Wu

  2. Department of Psychiatry, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan

    Hai-Gwo Hwu

  3. Institute of Statistics, National Central University, Chung-Li, Taiwan

    Yin-Jing Tien

Authors
  1. Chun-Houh Chen
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  2. Hai-Gwo Hwu
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  3. Wen-Jung Jang
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  4. Chiun-How Kao
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  5. Yin-Jing Tien
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  6. ShengLi Tzeng
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  7. Han-Ming Wu
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Editor information

Editors and Affiliations

  1. Faculty of Mathematics and Physics Department of Statistics and Probability, Charles University, Sokolovská 83, 18675, Prague 8 - Karlin, Czech Republic

    Jaromir Antoch

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© 2004 Springer-Verlag Berlin Heidelberg

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Chen, CH. et al. (2004). Matrix Visualization and Information Mining. In: Antoch, J. (eds) COMPSTAT 2004 — Proceedings in Computational Statistics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2656-2_6

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  • DOI: https://doi.org/10.1007/978-3-7908-2656-2_6

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1554-2

  • Online ISBN: 978-3-7908-2656-2

  • eBook Packages: Springer Book Archive

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